last update: 2019 12 14
- Artificial intelligence (AI).
- Artificial general intelligence (AGI).
- Recent history of AI.
Useful intelligence and useful work is the same.
Even the most primitive machine is intelligent.
AI 1 determines AI 2 by using many inputs that will resemble the expected inputs in the future.
The creation of AI by AI in a general way by the logic of trial and error and the judgement of outputs given certain inputs is a revolution in computer science.
This is not only passive machine programming but automated active machine learning by interaction with other forms of intelligence (human, AI).
Tactics of machine learning used commonly in 2019:
After many iterations, AI 2 will learn about AI 1 and make good use of AI 1.
The use of AI 1 becomes part of the new AI function.
Humans started to learn about nature (science) in a random way.
Abstractions (aka knowledge, concepts, names, formulas, functions) are passed from persons to persons.
The programmer knows the problem and the wanted solution.
This is quite personal.
An AI can learn and predict a person better than the person itself.
Still, the person or programmer is the required input for the AI.
How to create the input?
This is why a program language is used.
The human programmer has already recognized patterns.
A pattern is declared as a type or function in the program language.
The transition or translation from input patterns to output patterns is declared as a function.
AGI means general logic and intelligence.
An AGI can judge the relations of received inputs.
The sooner AGI is created with free access to information, the better.
Any AI can become evil and/or insane with wrong or limited information.
An AI can be intelligent in arbitrary ways.
Useful intelligence means useful work.
Many AI have already surpassed the intelligence of the most intelligent humans in certain tasks.
A neural network is different from many other programs in that a neural network is programmed to do general logic by discovering and learning what limits what.
Like all programs, a neural network requires data in a certain structure.
But to compute logic to discover functions (limitations) in the given data of input and output (fact and consequence) is an ability of a neural network.
The data can represent anything and therefore the neural network does general logic.
E.g. logic to discover the traits (things) that indicate or enforce that a thing is a dog.
A human does AGI.
AGI does not mean great intelligence.
The common tactics of machine learning (deep learning, genetic programming) are sufficient and general enough to implement AGI.
IMO limitations of AI in 2019:
Limited input and output for learning.
E.g. only data for playing games or for driving a car.
The Super Intelligence End Game (Jürgen Schmidhuber, Charlie Muirhead) | DLD17 (2017-01-23) time 633.
Correction of the video after 540:
The Super Intelligence End Game (Jürgen Schmidhuber, Charlie Muirhead) | DLD17 (2017-01-23) time 540.
How Many Neurons Are in the Brain? (2019-06-11)
Quote: "We found that on average the human brain has 86 billion neurons. And not one that we looked at so far has the 100 billion. Even though it may sound like a small difference the 14 billion neurons amount to pretty much the number of neurons that a baboon brain has or almost half the number of neurons in the gorilla brain. So that's a pretty large difference actually," explained Herculano-Houzel.
So, according to this new research, the human brain likely has somewhere around 86 billion neurons.
- Quote: "We found that on average the human brain has 86 billion neurons. And not one that we looked at so far has the 100 billion. Even though it may sound like a small difference the 14 billion neurons amount to pretty much the number of neurons that a baboon brain has or almost half the number of neurons in the gorilla brain. So that's a pretty large difference actually," explained Herculano-Houzel.
- AI programs must be created (designed and trained) to be useful.
Abstraction and reference are synonyms.
An abstraction of something is a reference to something.
- A word (a name) for something is an abstraction of something or a reference to something.
- A function application is an abstraction of something or reference to something.
- A hyperlink of the web is an abstraction of something or a reference to something.
Abstractions or references are utmost important for intelligence.
Abstractions or references are required technical optimizations.
The memory and computation power (electricity in computer chips) of a computer is limited.
A reference to a thing requires much less memory than the referenced thing.
The implementation of AI and intelligence in general is based on abstractions.
In the brain: Not written numbers are moved around but the idea of numbers implemented by the nervous system.
In a computer: Functions use functions.
Abstractions determine useful intelligence.
A less useful abstraction means less useful understanding and less useful relations or combinations with other abstractions.
The notation of a reference is a real limiting thing or implementation.
The presentation on a paper. The perception by the eye or the hand of what is presented. The processing by the brain.
Fundamental theorem of software engineering.
We can solve any problem by introducing an extra level of indirection.
Quote 2 from indirection:
In computer programming, indirection (also called dereferencing) is the ability to reference something using a name, reference, or container instead of the value itself.
Quote 3 from indirection:
A famous aphorism of David Wheeler goes: "All problems in computer science can be solved by another level of indirection" (the "fundamental theorem of software engineering").
This is often deliberately mis-quoted with "abstraction layer" substituted for "level of indirection".
An often cited corollary to this is, "...except for the problem of too many layers of indirection."
Abstraction and reference are synonyms.
Quote 4 from indirection:
A humorous Internet memorandum, RFC 1925, insists that:
- (6) It is easier to move a problem around (for example, by moving the problem to a different part of the overall network architecture) than it is to solve it.
- (6a) (corollary). It is always possible to add another level of indirection.
IMO regarding quote 4:
Logic is search and discovery of what is true.
Software and abstractions are often judged according to their utility to enable intelligence (human, AI) to do useful work.
The decision of how to solve what problem is very important for programmers.
The decision of what problem to solve at what abstraction layer is very important for programmers.
The distinction between "what to do" and "how to do" is the distinction between a function application (abstraction, reference) and a function implementation.
IBM Watson: Final Jeopardy! and the Future of Watson (2011-02-16).
The computer that mastered Go (2016-01-27).
DeepMind AI Reduces Google Data Centre Cooling Bill by 40% (2016-07-20).
AlphaGo Zero: Starting from scratch (2017-10-18).
A Chinese AI passed the national medical licensing exam, so technically it’s a doctor (2017-11-21).
Microsoft reaches a historic milestone, using AI to match human performance in translating news from Chinese to English (2018-03-14).
HN: Using AI to match human performance in translating news from Chinese to English (microsoft.com) (2018-03-15).
Google Duplex: A.I. Assistant Calls Local Businesses To Make Appointments (2018-05-08).
20 top lawyers were beaten by legal AI. Here are their surprising responses (2018-10-25).
Brain-to-Brain Communication is Coming! (2018-10-17).
This Curious AI Beats Many Games...and Gets Addicted to the TV (2018-11-17).
Building a Curious AI With Random Network Distillation (2018-12-02).
AlphaFold: Using AI for scientific discovery (2018-12-02).
AlphaFold @ CASP13: “What just happened?” (2018-12-09).
How one scientist coped when AI beat him at his life’s work (2019-02-15).
Inside DeepMind's epic mission to solve science's trickiest problem (2019-08-06).
This AI Learns From Humans…and Exceeds Them (2019-01-10).
DeepMind’s AlphaStar Beats Humans 10-0 (or 1) (2019-02-06).
New AI fake text generator may be too dangerous to release, say creators (2018-02-14).
AI can write just like me. Brace for the robot apocalypse (2019-02-15).
OpenAI Five Beats World Champion DOTA2 Team 2-0 (2019-05-18).
DeepMind Made a Math Test For Neural Networks (2019-06-04).
Article: Analysing Mathematical Reasoning Abilities of Neural Models (2019-04-02).
Capture the Flag: the emergence of complex cooperative agents (2019-05-30).
Video: Human-level in first-person multiplayer games with population-based deep RL (2018-07-06).
Aristo A.I. scores ‘A’ on 8th-grade science test (2019-09-04).
An A.I. called Aristo, developed by the Allen Institute, was able to correctly answer 90 percent of questions on a science exam designed for eighth graders.
However, the system is designed only to interpret language, meaning it can answer multiple choice questions, but not those featuring an illustration or graph.
But it was able to answer logic-based questions like this:
Which change would most likely cause a decrease in the number of squirrels living in an area?
(1) a decrease in the number of predators
(2) a decrease in competition between the squirrels
(3) an increase in available food
(4) an increase in the number of forest fires
OpenAI’s GPT-2 Is Now Available - It Is Wise as a Scholar! (2019-10-01).
HN: Google T5 scores 88.9 on SuperGLUE Benchmark, approaching human baseline (gluebenchmark.com) (2019-10-25).
r/singularity: Google T5 scores 88.9 on SuperGLUE Benchmark, compared to 89.8 human baseline (2019-10-25).